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Race and Analysis Using race in this context can be problematic. The state law lists five “races” –Caucasian –African American –Native American / Alaskan –Hispanic –Asian / Pacific Islander Unfortunately these categories do not conform very well to census categories.

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Census categories Census 2000 lists the following categories of race: –White –Black or African American –American Indian and Alaska Native –Asian –Hawaiian or Pacific Islander –Some other race –Two or more races (up to six) Note that the census bureau does not consider “Hispanic” a race!

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Hispanics as a race The census, however, in a separate question, asks all respondents if they are of “Hispanic origin”. About 12.5% of the participants identified themselves as Hispanic. All of those individuals also listed a race. Some said they were of more than one race—oft times indicating that the second “race” was Mexican or Cuban. 90% of people who said they were Hispanic listed their race as white!

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Reconciliation In order to match the state law we have done the following: –Anyone that listed themselves as Hispanic was categorized as Hispanic (PERF procedures). –Any person that reported that they were of two or more races (less than 2% of the total) was excluded except for those that indicated that they were Hispanic. They were counted as Hispanic. –Any person that listed their race as “some other” is excluded but 97% of them reported that they were Hispanic, and were thus included. –We merged Native Hawaiian/Pacific Islander with Asian

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Analytical Strategy Big Question One : To what extent, if any, does a driver’s race influence an officer’s decision to stop a vehicle for a traffic violation. Big Question Two: To what extent, if any, does race influence what happens after the stop. –Does race influence disposition? –Does race influence the decision to search the vehicle and or the driver?

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Race and the Stop Decision Two basic approaches: –Compare the proportion of stops of minority drivers with the minority population of drivers “at risk” for being stopped.  This, of course raises the question of what is the population “at risk”? What is the benchmark? –Examine the reason for stop by race. If race does not influence the decision to stop then reasons should look alike across races. First let’s look at methods that do not rely on a benchmark.

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Benchmarks We have seen three analyses (reason, disposition, search) that do not require a benchmark. These approaches examine the “universe” of stops and look at differences across the groups. This brings us back to examining the relative frequency of stops by race. Let’s say that in community X, 25% of all stops are for Hispanics. That does not provide much information unless we have some basis for comparison. Benchmarks provide that basis.

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What is a Benchmark? Benchmarks can allow us to understand whether any group is being stopped disproportionately. It tells us who is using the roadways. Helps us understand the “true” demographics of a community.

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Not all benchmarks are equal Strong Benchmarks –Provide meaningful information –Allow for high quality analysis –Can indicate the existence of a problem –Can indicate the degree, nature, and specifics of a problem. Weak Benchmarks –Disparities can be masked –Recommendations for future action are ineffective

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Why do we Adjust Census Data? Unadjusted Census data benchmarks are generally inaccurate: –can indicate a disparity where none exists –can “mask” (or hide) disparities –cannot rule out alternative hypotheses –have high “miss” rates for minorities Census data and stop data measure different populations: –Census data: the residents of a jurisdiction; stop data: both resident and non-residents –Census data: static populations; stop data: transient population Comparisons –to make a valid comparison, the two items compared must be the same –“matching the numerator to the denominator”

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Census Adjusted Benchmarks Age –residents under the age of 15 are generally not at risk of being stopped Location of the stop –greater police presence in an area causes greater risk of being stopped –different locations may have different driver demographics (near highways, in college towns, etc) –racial/ethnic groups tend to live in “clusters” Driving quantity of a racial/ethnic group –those without access to vehicles have lesser risk of being stopped –driving by racial/ethnic groups vary across days of the week, time of day, seasons Driving quality of a racial/ethnic group Influx of nonresident drivers –journey-to-work data –attractions inherent in a jurisdiction (shopping, entertainment, universities) –major highways

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Observational studies Involves “stationary” and “rolling” surveys –stationary: surveyors on street corners record the perceived race/ethnicity of a driver –rolling: surveyors in cars record the perceived race/ethnicity of a driver Based on the data gathered from the surveys, a demographic driver profile is created Stop/search data compared to observed data More this afternoon.

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Push /Pull Based on the idea that: –nonresidents enter a jurisdiction to shop, vacation, travel through the region, access entertainment, etc –residents leave/enter a jurisdiction to work Push values determined by considering (by race): –vehicle ownership –proportion of people who drive 10 or miles to work –driving time between jurisdictions Pull (“draw”) values determined for each jurisdiction based on: –percent state employment –percent of state retail trade –percent of state food and accommodation sales –percent of state average daily road volume The 2 values (for push and pull) were combined to determine the demographic profile of a driver in a given jurisdiction

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Traffic Accident Data Motorists “not-at-fault” in two vehicle traffic accidents provide a representative sample of the roadway demographics of a particular jurisdiction Not yet fully validated in a racial profiling data analysis setting May be difficult to implement as some jurisdictions do not record the race/ethnicity of motorists involved in accidents

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Our Benchmarking Plan NUCPS will use Adjusted Census Benchmarking: Includes persons 15 years or older. A community’s benchmark consists of two levels: –Community level –County level Theory is that the adjusted county driving population more accurately represents the driving population for the majority of communities. For example, The City of Lake Forest has an adjusted minority population of 7.2 % but Lake County has an adjusted minority population of 23.5%. Both of these will be used.

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Special Cases Cook County –We have constructed benchmarks for the six judicial districts. These more accurately reflect transportation patterns. For communities that are in more than one county or that border another county we can use both. County Sheriffs will use the county ISP can use state, county, district etc. Special Departments (university, park police) will use the closest area.

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What will our report look like? One statewide analysis and an analysis of every participating agency. No individual officer level analysis Modeled largely on other statewide systems, particularly Missouri. http://www.ago.state.mo.us/racialprofiling/racialprofiling.htm Period for review and agency comment.

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Summary of analysis Three non-benchmarked items: reason, disposition, search by individual race, and by white/minority. –Focus on consent searches Adjusted census benchmark based on community, county or judicial district (Cook County). –Comparison of stops by individual race, and by white/minority. Our report will indicate where disparities exist. We will not claim that these disparities indicate the presence of racial profiling. Your task will be to understand and explain the data to stakeholders.